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A Decentralized Technique for Robust Probabilistic Mixture Modelling of a Distributed Data Set

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Intelligent Distributed Computing V

Part of the book series: Studies in Computational Intelligence ((SCI,volume 382))

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Abstract

This paper deals with a machine learning task, namely probability density estimation, in the case data is composed of subsets hosted on nodes of a distributed system. Focusing on mixture models and assuming a set of local probability distribution estimates, we demonstrate how it is possible to combining local estimates in a dynamic, robust and decentralized fashion, through gossiping a global probabilistic model over the data set. Experiments are reported to illustrate the proposal.

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Author information

Authors and Affiliations

  1. LINA (UMR CNRS 6241), Université de Nantes, Nantes, France

    Ali El Attar, Antoine Pigeau & Marc Gelgon

Authors
  1. Ali El Attar
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  2. Antoine Pigeau
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  3. Marc Gelgon
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Editor information

Editors and Affiliations

  1. Faculty of Technology,Policy andManagement, Intelligent Interactive Dynamic Systems, Section Systems Engineering, Delft University of Technology, Jaffalaan 5, 2628BX, Delft, The Netherlands

    F. M. T. Brazier

  2. D-CIS lab, P.O. Box 90, 2600 AB, Delft, The Netherlands

    Kees Nieuwenhuis  & Gregor Pavlin  & 

  3. Faculty of Technology,Policy andManagement, Section Systems Engineering, Delft University of Technology, Jaffalaan 5, 2628BX, Delft, The Netherlands

    Martijn Warnier

  4. Faculty of Automatics, Computers and Electronics, Software Engineering Department, University of Craiova, Bvd. Decebal Nr. 107, 200440, Craiova, Romania

    Costin Badica

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© 2011 Springer-Verlag Berlin Heidelberg

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El Attar, A., Pigeau, A., Gelgon, M. (2011). A Decentralized Technique for Robust Probabilistic Mixture Modelling of a Distributed Data Set. In: Brazier, F.M.T., Nieuwenhuis, K., Pavlin, G., Warnier, M., Badica, C. (eds) Intelligent Distributed Computing V. Studies in Computational Intelligence, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24013-3_29

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  • DOI: https://doi.org/10.1007/978-3-642-24013-3_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24012-6

  • Online ISBN: 978-3-642-24013-3

  • eBook Packages: EngineeringEngineering (R0)

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